Predicting daily Citibike trips (open-ended)

Load data

trips_per_day <- read_tsv("trips_per_day.tsv")

Split the data into different sets

set.seed(42)
# sample split using caTools package
sample <- sample.split(trips_per_day$num_trips, SplitRatio = 0.8)
train_data <- subset(trips_per_day, sample== T)
other_data <- subset(trips_per_day, sample== F)
sample2 <- sample.split(other_data$num_trips, SplitRatio = 0.5)
validate_data <- subset(other_data, sample2== T)
test_data <- subset(other_data, sample2== F)
# Check if the split works
nrow(trips_per_day)==sum(nrow(train_data), nrow(test_data), nrow(validate_data))

## [1] TRUE

Modeling with only minimum tempurature as predictor

K <- 1:8
train_err <- c()
validate_err <- c()
for (k in K){
  # fit on the training data
  model <- lm(num_trips ~ poly(tmin, k, raw = T), train_data)
  
  # evaluate on the training data
  # RMSE-> root mean square error. sqrt( (predict-truth)^2 /number of data )
  train_err[k] <- sqrt(mean((predict(model, train_data)-train_data$num_trips)^2))
  validate_err[k] <- sqrt(mean((predict(model, validate_data ) - validate_data$num_trips)^2))
}

plot_data <- data.frame(K, train_err, validate_err) %>% 
  gather("split","error",-K)

ggplot(plot_data, aes(x=K, y = error, color = split)) +
  geom_line() +
  scale_x_continuous(breaks = K) +
  xlab('Polynomial Degrees') +
  ylab('RMSE')

Consider other predictors to improve the model

• add weekdays as another predictor

train_data <- train_data %>% 
  mutate(weekday = weekdays(as.POSIXct(ymd), abbreviate = T))
validate_data<- validate_data %>% 
  mutate(weekday = weekdays(as.POSIXct(ymd), abbreviate = T))

#Check if weekdays increases R^2 for the value
model <- lm(num_trips ~. , train_data)
glance(model)

• add whether is a major holiday as a predictor

major_holidays <- c("0101", "0120", "0214", "0217", "0526", "0704", "0901", "1013", "1127", "1224", "1225","1231")

train_data <- train_data %>% 
  mutate(month_day = format(train_data$ymd,"%m%d"),
         is_holiday = month_day %in% major_holidays) %>% 
  select(-month_day)

validate_data <- validate_data %>% 
  mutate(month_day = format(validate_data$ymd,"%m%d"),
         is_holiday = month_day %in% major_holidays) %>% 
  select(-month_day)

#Check if is_holiday increases R^2 for the value
model <- lm(num_trips ~. , train_data) 
glance(model)

• add is_flu_season as a predictor

flu_season <- c("12", "01", "02")
train_data <- train_data %>% 
  mutate(month = format(ymd,"%m"),
         is_flu_season = month %in% flu_season) %>% 
  select(-month)

validate_data <- validate_data %>%  mutate(month = format(ymd,"%m"),
         is_flu_season = month %in% flu_season) %>% 
  select(-month)

model <- lm(num_trips ~. -ymd -date -snow, train_data) 
glance(model)

Cross-Validation

# fit a model for each polynomial degree consider 7 predictors
K <- 1:8
train_err <- c()
validate_err <- c()
for (k in K){
  # fit on the training data
  model <- lm(num_trips ~ poly(prcp, k, raw = T) + poly(snwd, k, raw = T) + poly(tmax, k, raw = T) + poly(tmin, k, raw = T) + weekday + is_holiday + is_flu_season, train_data)
  
  # evaluate on the training data
  # RMSE-> root mean square error. sqrt( (predict-truth)^2 /number of data )
  train_err[k] <- sqrt(mean((predict(model, train_data)-train_data$num_trips)^2))
  validate_err[k] <- sqrt(mean((predict(model, validate_data ) - validate_data$num_trips)^2))
}

plot_data <- data.frame(K, train_err, validate_err) %>% 
  gather("split","error",-K)

ggplot(plot_data, aes(x=K, y = error, color = split)) +
  geom_line() +
  scale_x_continuous(breaks = K) +
  xlab('Polynomial Degrees') +
  ylab('RMSE')

Polynomial of Degree two gives lowest validation error. Further investigation still nedded.

Further defined the model at polynomial of degree two

# Everything at degree 2
model <- lm(num_trips ~ poly(prcp, 2, raw = T)+poly(snwd, 2, raw = T)+poly(tmax, 2, raw = T)+poly(tmin, 2, raw = T) + weekday + is_holiday + is_flu_season, train_data)
glance(model)

# take out insignificant predictors
model <- lm(num_trips ~ poly(prcp, 2, raw = T)+ snwd + poly(tmax, 2, raw = T)+poly(tmin, 2, raw = T) + weekday + is_holiday + is_flu_season, train_data)

# check R^2
rsquare(model, train_data)

## [1] 0.898932

rsquare(model, validate_data)

## [1] 0.9154361

# take out more insignificant predictors
model <- lm(num_trips ~ poly(prcp, 2, raw = T)+ snwd + tmax + tmin + weekday + is_holiday + is_flu_season, train_data)

#Check rmse and R^2 
rmse(model, train_data)

## [1] 3180.246

rmse(model, validate_data)

## [1] 3116.522

rsquare(model, train_data)

## [1] 0.8976948

rsquare(model, validate_data)

## [1] 0.9110483

Best model

# Best Model
model <- lm(num_trips ~ poly(prcp, 2, raw = T)+ snwd + tmax + tmin + weekday + is_holiday + is_flu_season, train_data)# 0.8976948
rsquare(model, train_data)

## [1] 0.8976948

tidy(model)

# test the model by combine train_data with validate_data
com_data <- rbind(train_data, validate_data)
rsquare(model, com_data)

## [1] 0.8996773

plot_data <- validate_data %>%
  add_predictions(model)

ggplotly(ggplot(plot_data, aes(x= ymd, y = pred))+
  geom_point(aes(y= num_trips))+
  geom_line(aes(y=pred), color = "red")+
  geom_point(aes(y=pred), color = "red") + 
  geom_smooth() +
  xlab("Date") +
  ylab("Predicted (in red)/ Actual (in black)")+
    ggtitle("Number of trips at different dates"))

ggplot(plot_data, aes(x=pred, y =num_trips ))+
  geom_point()+
  geom_abline(linetype = "dashed") +
  xlab('Predicted') +
  ylab('Actual')